To address the challenges of complex farmland environment, an intelligent obstacle avoidance control algorithm for agricultural unmanned aerial vehicles (UAVs) is developed. The objective is to solve the problem of efficient obstacle avoidance in farmland scenarios characterized by dense dynamic obstacles and variable terrain. In this article, a target detection algorithm based on improved YOLOv5 (You Only Look Once v5) is proposed, and an intelligent obstacle avoidance system is constructed by combining reinforcement learning path planning and adaptive motion control strategy. Ghost module is introduced to improve the lightweight of YOLOv5, and the design of CIOU (Complete Intersection Over Union) loss function is optimized, which improves the detection accuracy of the model for small targets and dynamic obstacles. Experiments show that the error of path planning is reduced to less than 2.1 meters, and the time consumption is reduced by about 35%. In addition, fuzzy logic controller is used to realize adaptive PID control, which further enhances the flight stability of UAV in complex environment. The results show that the improved algorithm has excellent performance in many typical farmland scenes. This study provides theoretical and technical support for autonomous flight of agricultural UAV in complex farmland environment.
Yuan et al. (Wed,) studied this question.